Peritoneal dialysis training and interventions: A narrative review
Bibliographic record
Abstract
BackgroundPeritoneal dialysis (PD) training and education for patients and their caregivers, provided by PD nurses, are crucial for effective PD programs. The goal is to impart sufficient knowledge, skills, training, and support to minimize complications. However, the evidence regarding effective educational interventions during training has been unclear and inconsistent. The review question was: How do PD training methods and educational interventions impact on PD outcomes in adult patients?MethodsA narrative review was undertaken with defined inclusion and exclusion criteria of articles published in the last 10 years. Databases were searched, followed by a selection process conducted with the project team. Quality appraisal and a final selection were uploaded to Excel, and data was extracted. A narrative description of the results was then completed.ResultsA total of 982 articles followed the selection process of these 21 studies, including mixed methods research design, but all met the inclusion criteria. The results were described under headings of training methods, educational interventions, patient characteristics, retraining, and outcomes reported.ConclusionsThe narrative review highlights gaps in robust evidence for educational interventions during training. However, some evidence supports adapting PD training methods to incorporate more individualized approaches, appropriate pre-training assessments, and consistent outcome measures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".